Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of features, which enables better interpretability than feature extraction. However, most existing feature selection approaches, especially deep-learning-based, often focus on the features with great importance scores only but neglect those with less importance scores during training as well as the order of important candidate features. This can be risky since some important and relevant features might be unfortunately ignored during training, leading to suboptimal solutions or misleading selections. In our work, we deal with feature selection by exploiting the features with less importance scores and propose a feature selection framework based on a novel complementary feature mask. Our method is generic and can be easily integrated into existing deep-learning-based feature selection approaches to improve their performance as well. Experiments have been conducted on benchmarking datasets and shown that the proposed method can select more representative and informative features than the state of the art.
C$^{2}$IMUFS: Complementary and Consensus Learning-based Incomplete Multi-view Unsupervised Feature Selection
Southwestern University of Finance and Economics（西南财经大学）；JD Intelligent Cities Research（京东智能城市研究院）；JD Intelligent Cities Business Unit（京东智能城市事业部）；University of Central Florida（中佛罗里达大学）；Southwest Jiaotong University（西南交通大学）
Mixture of Complementary Agents for Robust LLM Ensemble
DIMACS, Rutgers University（罗格斯大学DIMACS研究中心）；Department of Mathematics, Rutgers University（罗格斯大学数学系）；Department of Computer Science, George Mason University（乔治·梅森大学计算机科学系）；Department of Computer Science, Rutgers University（罗格斯大学计算机科学系）
One-step lowest-variance selection in a Gaussian random-field model motivated by masked diffusion: Total correlation and a square root collision threshold
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Wuhan University（武汉大学）；Southern University of Science and Technology（南方科技大学）；Peking University（北京大学）；University of Electronic Science and Technology of China（电子科技大学）
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Germany（人工智能与社会、科学与技术部门，柏林Zuse研究所，德国）；Institute of Mathematics, Technische Universität Berlin, Germany（数学研究所，柏林技术大学，德国）
